PREFERRED EMBODIMENT OF THE INVENTION

[0038] Referring to FIG. 1, a pipeline 1 connects a well (not shown) to an oil receiving facility which includes measurement apparatus 2 and capture tank 3 for catching slugs. The measurement apparatus 2 and capture tank 3 are located on a platform 4 such as an oil rig or recovery vessel. Analyser apparatus 5 is also provided to receive signals from the measurement apparatus 2 so to provide control signals for reporting the state of the capture tank 3.

[0039] Referring to FIG. 2, the measurement apparatus 2 is used to measure changes in content 6 flowing through the pipeline 1. A common problem is the appearance of slugs 7 in a multiphase mixture of oil 8 and water 9, and gas 10.

[0040] To measure changes in the contents flowing through the pipe first and second measurement devices 11₁, 11₂ are employed. In this example, the measurement devices 11₁, 11₂ are in the form of gamma ray (γ-ray) densitometers. Each measurement device 11₁, 11₂ comprises a respective source 12₁, 12₂ of γ-rays, which produces a beam 13₁, 13₂ of γ-rays that pass through the pipe 1 and pipe contents 6. The beams 13₁, 13₂ are attenuated by the pipe 1 and the pipe contents 6. The beams 13₁, 13₂ are picked up by respective detectors 14₁, 14₂ which produce respective time-varying signals 15₁, 15₂.

[0041] Referring to FIG. 3, each detector 14₁, 14₂ comprises a respective receiver 16₁, 16₂ whose output is amplified using an amplifier 17₁, 17₂.

[0042] Referring to FIG. 4, the signals 15₁, 15₂ from the measurement devices 11₁, 11₂ are fed into the analysing apparatus 5 via ports 18₁, 18₂. The signals 15₁, 15₂ are fed through filters 19₁, 19₂ to a multi-channel analog-to-digital converter card 20 which samples of the signals 15₁, 15₂ and feeds the samples to a processor 21. The analyser apparatus 5 further includes random access memory (RAM) 22, flash memory 23, a channel output control 24 for providing an estimate of slug volume, a display 25, in the form of a monitor, a user input 26, preferably in the form of a keyboard and mouse, an output 27, in the form of an RS-232 port, and storage medium 28, such as a hard disk.

[0043] In this example, the analysing apparatus 5 is in the form of a personal computer executing a computer program for performing a method of slug detection. However, dedicated hardware may be used.

[0044] Referring to FIG. 5, the analysing apparatus includes a graphical user interface in the form of a window 29 which is displayed on the monitor 25. The window 29 includes graphical representations 30₁, 30₂ of the signals 15₁, 15₂ (FIG. 2). Each graphical representation 30₁, 30₂ includes markers 31₁, 31₂ for indicating a front of a slug passing each measurement device 11₁, 11₂ (FIG. 2) and markers 32₁, 32₂ for indicating an end of a slug.

[0045] The window 29 displays slug parameters including positions 33₁, 33₂, 34₁, 34₂ of the markers 31₁, 31₂, 32₁, 32₂, velocities 35, 36 of the front and end of the slug, length 37 of the slug and estimated time of arrival (ETA) 38 of the slug at catcher 3 (FIG. 1). Four values of slug length are computed using the 33₁, 33₂, 34₁, 34₂ of the markers 31₁, 31₂, 32₁, 32₂, together with a value of slug velocity. The window 29 includes control buttons 39 for starting and stopping analysis.

[0046] Values for estimated time of arrival (ETA) 38 of the slug and slug volume are supplied as digital signals to a serial port 27 and to the control output 24 via a digital-to-analog output (not shown) with a 20 mA loop interface.

[0047] Referring to FIG. 6, a process for logging samples of the signals 15₁, 15₂ and analysing the signals 15₁, 15₂ so as to detect locations of front and end of a slug 7 (FIG. 2) and compute slug parameters is shown.

[0048] A user starts the process and the analyser apparatus 5 (FIG. 1) is initialised, for example by clearing buffers and resetting software flags (step S1).

[0049] A period of “running-in” occurs. The analyser apparatus 5 receives signals 15₁, 15₂ from the measurement devices 11₁, 11₂ (FIG. 2) and samples them at known rate (step S2). In this example, the sampling rate is 10 samples per second.

[0050] Referring also to FIG. 7, samples 15₁′, 15₂′ of the signals 15₁, 15₂ are stored in first and second buffers 40₁, 40₂ respectively (step s3). In this example, each buffer 40₁, 40₂ is able to store 3,000 samples and is configured in a so-called “first in, first out” (FIFO) arrangement. The process continues until each buffer 40₁, 40₂ is filled (step S4). Once the buffers 40₁, 40₂ are filled, then analysis of the signals 15₁, 15₂ can begin (step S5).

[0051] Three different processes for detecting slugs may be used. Preferably, all three processes are used, although results of one of the processes may be selected and used.

[0052] Differentiated Waveform Process

[0053] Referring to FIG. 8, a first process for detecting slugs is shown:

[0054] For each signal 15₁, 15₂, a graphical representation 30₁, 30₂ is displayed (step S5.1.1). A further predetermined number of samples 15₁′, 15₂′ of the signals 15₁, 15₂, herein referred to as a data set, are acquired and added to the front of each respective buffer 40₁, 40₂ (steps S5.1.2 & S5.1.3). A corresponding number of samples are deleted from the end of each buffer 40₁, 40₂. In this example, the data set comprises 10 samples corresponding to 1 second.

[0055] For each buffer 40₁, 40₂, the signals 15₁, 15₂ are processed before slug detection occurs.

[0056] Each signal 15₁, 15₂ is amplified, in this case by a factor of 1,000, to produce a corresponding amplified signal (step S5.1.4). The amplified signal is then smoothed to produce a smoothed signal (step S5.1.5). The smoothed signal is differentiated to produce a differentiated signal (step S5.1.6). The differentiated signal is then non-linearly amplified, in this case by squaring the differentiated signal and preserving its sign (step S5.1.7). The result is a processed signal suitable for searching for features which correspond to and which identify slug boundaries.

[0057] Referring to FIG. 9, each processed signal comprises a plurality of samples 41, in this case 3000. The processed signal is examined to identify locations where it crosses the sample axis, in other words to identify so-called “zero crossings”. The processed signal is divided into periods 42₁, 42₂, 42₃, 42₄, 42₅, herein referred to as epochs, between the zero crossings. For each epoch 42₁, 42₂, 42₃, 42₄, 42₅, a maximum magnitude value 43₁, 43₂, 43₃, 43₄, 43₅ is determined. This is performed using a time encoded signal processing and recognition (TESPAR) process.

[0058] A description of the TESPAR process is found in GB-A-2145864, which is incorporated herein by reference.

[0059] For each processed signal, a probability distribution function, for example a Gaussian distribution, is used to determine a standard deviation δ (step S5.1.9), i.e., the set of epoch amplitudes that lie within 95% of the total set.

[0060] Each processed signal is examined for predefined features which are indicative of a slug boundary.

[0061] To identify a front of a slug, a search is made for epochs 42₁, 42₂, 42₃, 42₄, 42₅ whose maximum magnitude value 43₁, 43₂, 43₃, 43₄, 43₅ fall outside one standard deviation, i.e. whose absolute values |s|>|δ|. In FIG. 9, one such sample 45 is shown whose value 43₁, falls outside one standard deviation.

[0062] Each sample 45 whose value 43 falls outside one standard deviation and is positive is considered to be a front of a slug (step S5.1.10).

[0063] A check is made whether the samples 45 corresponding to a front of a slug are found within a predetermined window of time (step S5.1.11). In this example, the predetermined window of time corresponds to the latest 10 samples acquired. If no samples 45 corresponding to a front of a slug are found in the predetermined window, then process returns to step S5.1.1 where the graphical representation 30₁, 30₂ (FIG. 5) are updated. Otherwise, the front of the slug is marked.

[0064] For each signal 15₁, 15₂, checks are made as to whether the front marker is valid (S5.1.12). For example, the front marker is checked to ensure that it is later in time than the currently held front markers. If the front marker is not later in time than the current marker then it is discarded. If the front marker is found to be within half a second of the currently held front marker, then the front marker is also checked to see whether the amplitude of the sample 15′ corresponding to the front marker, i.e. the measured density at the front of the slug, is greater than the amplitude of the sample corresponding to the current held front marker. If the front marker is found to be valid, then it becomes a new current marker (S5.1.14), otherwise the process returns to step S5.1.1.

[0065] For each signal 15₁, 15₂, a threshold density is calculated. The threshold density d_thesh is defined as being 60% of a difference between the density d₀ which lies in front of the slug, referred to as the “slug front porch density”, and the density d_F at the front of the slug, referred to as the “front marker density”.

[0066] Using a corresponding value of threshold density, each signal 15₁, 15₂ is searched to find the rear of the slug (step S5.1.16). The search may be limited to a predetermined window, for example, the latest 10 samples. If no rear of the slug is identified then the process returns to step S5.1.1. If the rear of the slug is found, then the sample location becomes the rear marker. However, the rear marker may be checked. For example, if a constraint is placed that the rear marker must be found within the latest 10 samples, then the additional check comprises determining the mean values for the first 5 samples and the second 5 samples. If both calculated values of mean are below the 60% threshold d_thesh and the two subsequent means have a negative slope, then the rear marker is valid.

[0067] Steps S5.1.1 to steps S5.1.17 are performed in respect of each signal 15₁, 15₂ to find front and end markers 31₁, 31₂, 32₁, 32₂ (FIG. 5). Once front and rear markers 31₁, 31₂, 32₁, 32₂ have been identified, then slug characteristics can be computed (S5.1.18).

[0068] For example, if the front of the slug is determined to be at time t₁ for the first signal 15₁ and the front of the slug is determined at time t₂ for the first signal 15₁ and the measurement devices 11₁, 11₂ are separated by a known distance D, then the velocity v_F of the front of the slug 7 may be calculated using: 1$\begin{array}{cc}{v}_F=\frac{D}{t_1-t_2}& \left(1\right)\end{array}$

[0069] Similarly, if the end of the slug for the first signal 15₁ occurs at time t₃ and the end of the slug occurs at time t₄ for a signal 15₂, then the velocity of v_R for the rear of the slug is defined as: 2$\begin{array}{cc}{v}_R=\frac{D}{t_3-t_4}& \left(2\right)\end{array}$

[0070] The length L of the slug is determined by calculating lengths L₁, L₂, L₃, L₄ of the slug using t₁, t₂, t₃, t₄, v_F, v_R using:

L₁=v_F(t₃−t₁) (3a)

L₂=v_F(t₄−t₂) (3b)

L₃=v_R(t₃−t₁) (3c)

L₄=v_R(t₄−t₂) (3d)

[0071] and then: 3$\begin{array}{cc}L=\frac{L_1+L_2+L_3+L_4}{4}& \left(4\right)\end{array}$

[0072] An estimated time T of arrival is calculated using: 4$\begin{array}{cc}T=\frac{1}{v_F}S& \left(5\right)\end{array}$

[0073] where S is the distance between the measurement apparatus 2 and the separator 3.

[0074] The slug characteristics 33₁, 33₂, 34₁, 34₂, 35, 36, 37, 38 (FIG. 5) are displayed and are also logged onto disk 28, for example in ASCII format together with a header file (FIG. 4) (steps S5.1.19 & S5.1.20)

[0075] A check is made whether the stop button 39 (FIG. 5) has been pressed (step S5.1.21). If the stop button has been pressed, indicating that the user no longer wishes to continue, then the process ends. Otherwise, the process returns to S5.1.1 where the signals 15₁, 15₂ are updated.

[0076] The slug volume can be used to control an input control valve (not shown) to the catcher 3 to prevent the catcher from overflowing.

[0077] Whole Waveform Process

[0078] Referring to FIG. 10, a second process for detecting slugs is shown:

[0079] For each signal 15₁, 15₂, a graphical representation 30₁, 30₂ is displayed (step S5.2.1). A further predetermined number of samples 15₁′, 15₂′ of the signals 15₁, 15₂, herein referred to as a data set, are acquired and added to the end of each respective buffer 40₁, 40₂ (steps S5.2.2 & S5.2.3). A corresponding number of samples are deleted from the front of each buffer 40₁, 40₂. In this example, the data set comprises 10 samples.

[0080] For each buffer 40₁, 40₂, the signals 15₁, 15₂ are processed before slug detection occurs.

[0081] Each signal 15₁, 15₂ is filtered using a Savitzky Golay filter (step S5.2.4). The Savitzky Golay filter has the advantage of reducing noise, while preserving features of interest. The Savitzky Golay filter is described in more detail in Section 8.3.5 in “Introduction to Signal Processing” by Sophocles J. Orfanidis, p434 (Prentice Hall) [ISBN 0-13-209172-0].

[0082] Referring to FIGS. 11 and 12, for each signal 15₁, 15₂, a density offset 46 is calculated (step S5.2.5) which is subtracted from the filtered signal.

[0083] A method of calculating the density offset 46 will now be described:

[0084] A base level threshold zx is calculated by taking the mean of the samples z in a buffer 40₁, 40₂, which may be described in pseudo-code as:

zx=mean(buf) where buf is the contents of the buffer

[0085] A deviation from the mean pv from the value zx is then calculated by taking the mean of the values z-zx, for values of z which are greater than zx, i.e.:

pv=mean(buf(find(buf>=zx) )−zx)

[0086] A deviation from the mean nv from the value zx is then calculated by taking the mean of the values zx−z, for values z which are less than zx, i.e.:

nv=mean(zx−buf(find(buf<zx)))

[0087] The offset zz 46 is calculated by subtracting the mean deviation nv from the base threshold zx and adding the average value of the deviations pv and nv, in other words:

zz=zx−nv+( (nv+pv)*0.5)

[0088] The result is a processed signal suitable for searching for features which correspond to and which identify slug boundaries.

[0089] Referring to FIGS. 12 and 13, each processed signal comprises a plurality of samples 47, in this case 3000. The processed signal is examined to identify locations 48₁, 48₂, 48₃, 48₄, 48₅, 48₆ where it has zero amplitude, in other words to identify so-called “zero crossings”. This is performed using a time encoded signal processing and recognition (TESPAR) process.

[0090] A description of a TESPAR process is found in GB-A-2145864, which is incorporated herein by reference.

[0091] To identify a front of a slug, a search is made for zero crossings 48₁, 48₂, 48₃, 48₄, 48₅,48₆ which positive going, in other words crossing negative to positive in the direction of time (step S5.2.8). In FIG. 12, zero crossings 48₂, 48₄, 48₆ are considered to be a front of a slug

[0092] A check is made whether the zero crossings 48₂, 48₄, 48₆ are found within a predetermined window of time (step S5.2.9). In this example, the predetermined window of time corresponds to the latest ₁₀ samples acquired. If no zero crossings 48₂, 48₄, 48₆ are found in the predetermined window, then process returns to step S5.2.1 where the graphical representation 30₁, 30₂ (FIG. 5) are updated. Otherwise, the front of the slug is marked

[0093] For each signal 15₁, 15₂, checks are made as to whether the front marker is valid (step S5.2.10 & S5.2.11). For example, the front marker is checked to ensure that it is later in time than the currently held front markers. If the front marker is not later in time than the current marker then it is discarded. If the front marker is found to be within half a second of the currently held front marker, then the front marker is also checked to see whether the amplitude of the sample 15′ corresponding to the front marker, i.e. the measured density at the front of the slug, is greater than the amplitude of the sample corresponding to the current held front marker. If the front marker is found to be valid, then it becomes a new current marker (step S5.2.12), otherwise the process returns to step S5.1.1.

[0094] To identify an end of a slug, a search is made for zero crossings 48₁, 48₂, 48₃, 48₄, 48₅, 48₆ which are negative going. (step S5.2.13). The search may be limited to a predetermined window, for example, the latest 10 samples. If no rear of the slug is identified then the process returns to step S5.2.1. If the rear of the slug is found, then the sample location becomes the rear marker. However, the rear marker may be checked. For example, if a constraint is placed that the rear marker must be found within the latest 10 samples, then the additional check comprises determining the mean values for the first 5 samples and the second 5 samples. If both calculated values of mean are below the 60% threshold d_thesh, then the rear marker is valid in a manner described earlier.

[0095] Steps S5.2.1 to steps S5.2.17 are performed in respect of each signal 15₁, 15₂ to find front and end markers 31₁, 31₂, 32₁, 32₂ (FIG. 5). Once front and end markers 31₁, 31₂, 32₁, 32₂ have been identified, then slug characteristics can be computed (S5.2.15) in a manner hereinbefore described.

[0096] The slug characteristics 33₁, 33₂, 34₁, 34₂, 35, 36, 37, 38 (FIG. 5) are displayed and are also logged onto disk 28, for example in ASCII format together with a header file (FIG. 4) (steps S5.2.16 & S5.2.17)

[0097] A check is made whether the stop button 39 (FIG. 5) has been pressed (step S5.2.18). If the stop button has been pressed, indicating that the user no longer wish to continue, then the process ends. Otherwise, the process returns to S5.2.1 where the signals 15₁, 15₂ are updated.

[0098] Process Including Non-Linear Filtering

[0099] Referring to FIG. 14, a third process for detecting slugs is shown.

[0100] For each signal 15₁, 15₂, a graphical representation 30₁, 30₂is displayed (step S5.3.1). A further predetermined number of samples 15₁′, 15₂′ of the signals 15₁, 15₂, herein referred to as a data set, are acquired and added to the end of each respective buffer 40₁, 40₂ (steps S5.3.2 & S5.3.3). A corresponding number of samples are deleted from the end of each buffer 40₁, 40₂. In this example, the data set comprises 10 samples.

[0101] For each buffer 40₁, 40₂, a signal 15 is processed before slug detection occurs. A representation 49 of a signal 15 held in a buffer 40 before processing is shown in FIG. 15 and a corresponding distribution 50 of values of density in the buffer 40 is shown in FIG. 16 (step 5.3.4).

[0102] Referring to FIGS. 17 and 18, the signal 15 is filtered using a low-pass, 8^th order Butterworth filter with a 0.5 Hz cut-off (step S5.3.4). The filtered signal 51 is shown in the frequency- and time-domains. A corresponding bipolar distribution 52 of the filtered signal 51 is shown in FIG. 19.

[0103] A density offset is calculated in a similar manner to calculation of density offset 46 and which is subtracted from the filtered signal (step S5.3.5 & S5.3.6).

[0104] The density offset is removed from the linearly filtered signal 51, which is unipolar, to create a bipolar filtered signal 51′ (FIG. 20) having the same density range.

[0105] Referring to FIG. 21, the bipolar linearly filtered signal 51′ is non-linearly filtered by taking the 10^th root of the modulus of the signal 51′ and retaining the sign to produce a non-linearly filtered signal 53 (step S5.3.7). A corresponding density distribution 54 of the non-linearly filtered signal 53 is shown in FIG. 22.

[0106] Referring to FIG. 21, the non-linearly filtered signal 53 appears as a pseudo-binary signal having a high state H and a low state L. However, the signal 53 includes a short duration feature X. The signal 53 is further processed to remove short features, such as feature X (step S5.3.8).

[0107] Zero crossings are identified. The duration between adjacent zero crossings is determined. If the duration is less than a predetermined value, for example 25 samples, then the region between the zero-crossings is considered to be a short feature. The short feature is removed by determining sign and amplitude of the portion of signal 53 between the adjacent zero-crossings and adding a further signal of opposite sign and same magnitude to said signal portion.

[0108] Referring to FIG. 23, a processed signal 55 is shown from which feature X has been removed.

[0109] Each signal 15₁, 15₂ is processed in the manner hereinbefore described to obtain a binary signal.

[0110] The front and rear of the slug are detected in a manner substantially similar that described in steps S5.2.8 to S5.2.18 (steps S5.3.9 to S 5.3.18).

[0111] Referring to FIG. 23 and 24, examples of pseudo binary signals 55, 56 obtained using the non-linear filtering process using first and second signal 15₁, 15₂ from measurement devices 11₁, 11₂ separated along a pipe 1 (FIG. 1) are shown. The signals 55, 56 show a delay in signal transition. Table I below tabulates values at which transitions occur for the first and second signals 15₁, 15₂ and a corresponding difference from which slug velocity may be calculated. 1

[0112] It will be appreciated that many modifications may be made to the embodiment described above.